164 related articles for article (PubMed ID: 38545735)
21. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma.
Zhang B; He X; Ouyang F; Gu D; Dong Y; Zhang L; Mo X; Huang W; Tian J; Zhang S
Cancer Lett; 2017 Sep; 403():21-27. PubMed ID: 28610955
[TBL] [Abstract][Full Text] [Related]
22. Distinguishing early-stage nasopharyngeal carcinoma from benign hyperplasia using intravoxel incoherent motion diffusion-weighted MRI.
Ai QY; King AD; Chan JSM; Chen W; Chan KCA; Woo JKS; Zee BCY; Chan ATC; Poon DMC; Ma BBY; Hui EP; Ahuja AT; Vlantis AC; Yuan J
Eur Radiol; 2019 Oct; 29(10):5627-5634. PubMed ID: 30903340
[TBL] [Abstract][Full Text] [Related]
23. Development and Validation of a Machine Learning Algorithm Predicting Emergency Department Use and Unplanned Hospitalization in Patients With Head and Neck Cancer.
Noel CW; Sutradhar R; Gotlib Conn L; Forner D; Chan WC; Fu R; Hallet J; Coburn NG; Eskander A
JAMA Otolaryngol Head Neck Surg; 2022 Aug; 148(8):764-772. PubMed ID: 35771564
[TBL] [Abstract][Full Text] [Related]
24. Pretreatment age and serum lactate dehydrogenase as predictors of synchronous second primary cancer in patients with nasopharyngeal carcinoma.
Yeh CF; Ho CY; Chin YC; Shu CH; Chao YT; Lan MY
Oral Oncol; 2020 Nov; 110():104990. PubMed ID: 32932171
[TBL] [Abstract][Full Text] [Related]
25. Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients.
Xiao Z; Song Q; Wei Y; Fu Y; Huang D; Huang C
Transl Cancer Res; 2023 Dec; 12(12):3581-3590. PubMed ID: 38192980
[TBL] [Abstract][Full Text] [Related]
26. Complementary roles of MRI and endoscopic examination in the early detection of nasopharyngeal carcinoma.
King AD; Woo JKS; Ai QY; Chan JSM; Lam WKJ; Tse IOL; Bhatia KS; Zee BCY; Hui EP; Ma BBY; Chiu RWK; van Hasselt AC; Chan ATC; Lo YMD; Chan KCA
Ann Oncol; 2019 Jun; 30(6):977-982. PubMed ID: 30912815
[TBL] [Abstract][Full Text] [Related]
27. A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study.
Zhong L; Dong D; Fang X; Zhang F; Zhang N; Zhang L; Fang M; Jiang W; Liang S; Li C; Liu Y; Zhao X; Cao R; Shan H; Hu Z; Ma J; Tang L; Tian J
EBioMedicine; 2021 Aug; 70():103522. PubMed ID: 34391094
[TBL] [Abstract][Full Text] [Related]
28. Potential factors associated with clinical stage of nasopharyngeal carcinoma at diagnosis: a case-control study.
Ren JT; Li MY; Wang XW; Xue WQ; Ren ZF; Jia WH
Chin J Cancer; 2017 Sep; 36(1):71. PubMed ID: 28870229
[TBL] [Abstract][Full Text] [Related]
29. Construction of diagnostic and prognostic models based on gene signatures of nasopharyngeal carcinoma by machine learning methods.
Wang Y; He Y; Duan X; Pang H; Zhou P
Transl Cancer Res; 2023 May; 12(5):1254-1269. PubMed ID: 37304552
[TBL] [Abstract][Full Text] [Related]
30. Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images.
Du D; Feng H; Lv W; Ashrafinia S; Yuan Q; Wang Q; Yang W; Feng Q; Chen W; Rahmim A; Lu L
Mol Imaging Biol; 2020 Jun; 22(3):730-738. PubMed ID: 31338709
[TBL] [Abstract][Full Text] [Related]
31. Machine learning algorithms for early sepsis detection in the emergency department: A retrospective study.
Kijpaisalratana N; Sanglertsinlapachai D; Techaratsami S; Musikatavorn K; Saoraya J
Int J Med Inform; 2022 Apr; 160():104689. PubMed ID: 35078027
[TBL] [Abstract][Full Text] [Related]
32. Evaluation of plasma Epstein-Barr virus DNA load to distinguish nasopharyngeal carcinoma patients from healthy high-risk populations in Southern China.
Ji MF; Huang QH; Yu X; Liu Z; Li X; Zhang LF; Wang P; Xie SH; Rao HL; Fang F; Guo X; Liu Q; Hong MH; Ye W; Zeng YX; Cao SM
Cancer; 2014 May; 120(9):1353-60. PubMed ID: 24477877
[TBL] [Abstract][Full Text] [Related]
33. Prediction and Evaluation of Machine Learning Algorithm for Prediction of Blood Transfusion during Cesarean Section and Analysis of Risk Factors of Hypothermia during Anesthesia Recovery.
Ren W; Li D; Wang J; Zhang J; Fu Z; Yao Y
Comput Math Methods Med; 2022; 2022():8661324. PubMed ID: 35465016
[TBL] [Abstract][Full Text] [Related]
34. Development of machine learning models to predict lymph node metastases in major salivary gland cancers.
Costantino A; Canali L; Festa BM; Kim SH; Spriano G; De Virgilio A
Eur J Surg Oncol; 2023 Sep; 49(9):106965. PubMed ID: 37393130
[TBL] [Abstract][Full Text] [Related]
35. Establishment of VCA and EBNA1 IgA-based combination by enzyme-linked immunosorbent assay as preferred screening method for nasopharyngeal carcinoma: a two-stage design with a preliminary performance study and a mass screening in southern China.
Liu Y; Huang Q; Liu W; Liu Q; Jia W; Chang E; Chen F; Liu Z; Guo X; Mo H; Chen J; Rao D; Ye W; Cao S; Hong M
Int J Cancer; 2012 Jul; 131(2):406-16. PubMed ID: 21866545
[TBL] [Abstract][Full Text] [Related]
36. Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.
Zhang B; Lian Z; Zhong L; Zhang X; Dong Y; Chen Q; Zhang L; Mo X; Huang W; Yang W; Zhang S
BMC Cancer; 2020 Jun; 20(1):502. PubMed ID: 32487085
[TBL] [Abstract][Full Text] [Related]
37. Computer-aided diagnosis and regional segmentation of nasopharyngeal carcinoma based on multi-modality medical images.
Qi Y; Li J; Chen H; Guo Y; Yin Y; Gong G; Wang L
Int J Comput Assist Radiol Surg; 2021 Jun; 16(6):871-882. PubMed ID: 33782844
[TBL] [Abstract][Full Text] [Related]
38. Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study.
Zhang L; Dong D; Li H; Tian J; Ouyang F; Mo X; Zhang B; Luo X; Lian Z; Pei S; Dong Y; Huang W; Liang C; Liu J; Zhang S
EBioMedicine; 2019 Feb; 40():327-335. PubMed ID: 30642750
[TBL] [Abstract][Full Text] [Related]
39. Prediction of low cardiac output syndrome in patients following cardiac surgery using machine learning.
Hong L; Xu H; Ge C; Tao H; Shen X; Song X; Guan D; Zhang C
Front Med (Lausanne); 2022; 9():973147. PubMed ID: 36091676
[TBL] [Abstract][Full Text] [Related]
40. Prediction of acute kidney injury in patients with liver cirrhosis using machine learning models: evidence from the MIMIC-III and MIMIC-IV.
Tian J; Cui R; Song H; Zhao Y; Zhou T
Int Urol Nephrol; 2024 Jan; 56(1):237-247. PubMed ID: 37256426
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]